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math.OC2025
A linesearch-based derivative-free method for noisy black-box problems
Alberto De Santis, Giampaolo Liuzzi, Stefano Lucidi
In this work we consider unconstrained optimization problems. The objective function is known through a zeroth order stochastic oracle that gives an estimate of the true objective…
math.OC2025
On the Batch Size Selection in Stochastic Gradient Methods Using No-Replacement Sampling
Marco Boresta, Alberto De Santis, Stefano Lucidi
Recent stochastic gradient methods that have appeared in the literature base their efficiency and global convergence properties on a suitable control of the variance of the gradien…
math.OC2023
A clustering heuristic to improve a derivative-free algorithm for nonsmooth optimization
Manlio Gaudioso, Giampaolo Liuzzi, Stefano Lucidi
In this paper we propose an heuristic to improve the performances of the recently proposed derivative-free method for nonsmooth optimization CS-DFN. The heuristic is based on a clu…